Transcriptomic data meta-analysis reveals common and injury model specific gene expression changes in the regenerating zebrafish heart.

Zebrafish have the capacity to fully regenerate the heart after an injury, which lies in sharp contrast to the irreversible loss of cardiomyocytes after a myocardial infarction in humans. Transcriptomics analysis has contributed to dissect underlying signaling pathways and gene regulatory networks in the zebrafish heart regeneration process. This process has been studied in response to different types of injuries namely: ventricular resection, ventricular cryoinjury, and genetic ablation of cardiomyocytes. However, there exists no database to compare injury specific and core cardiac regeneration responses. Here, we present a meta-analysis of transcriptomic data of regenerating zebrafish hearts in response to these three injury models at 7 days post injury (7dpi). We reanalyzed 36 samples and analyzed the differentially expressed genes (DEG) followed by downstream Gene Ontology Biological Processes (GO:BP) analysis. We found that the three injury models share a common core of DEG encompassing genes involved in cell proliferation, the Wnt signaling pathway and genes that are enriched in fibroblasts. We also found injury-specific gene signatures for resection and genetic ablation, and to a lower extent the cryoinjury model. Finally, we present our data in a user-friendly web interface that displays gene expression signatures across different injury types and highlights the importance to consider injury-specific gene regulatory networks when interpreting the results related to cardiac regeneration in the zebrafish. The analysis is freely available at: https://mybinder.org/v2/gh/MercaderLabAnatomy/PUB_Botos_et_al_2022_shinyapp_binder/HEAD?urlpath=shiny/bus-dashboard/

Matching single cells across modalities with contrastive learning and optimal transport.

Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching

LnCompare: gene set feature analysis for human long non-coding RNAs.

Interest in the biological roles of long noncoding RNAs (lncRNAs) has resulted in growing numbers of studies that produce large sets of candidate genes, for example, differentially expressed between two conditions. For sets of protein-coding genes, ontology and pathway analyses are powerful tools for generating new insights from statistical enrichment of gene features. Here we present the LnCompare web server, an equivalent resource for studying the properties of lncRNA gene sets. The Gene Set Feature Comparison mode tests for enrichment amongst a panel of quantitative and categorical features, spanning gene structure, evolutionary conservation, expression, subcellular localization, repetitive sequences and disease association. Moreover, in Similar Gene Identification mode, users may identify other lncRNAs by similarity across a defined range of features. Comprehensive results may be downloaded in tabular and graphical formats, in addition to the entire feature resource. LnCompare will empower researchers to extract useful hypotheses and candidates from lncRNA gene sets

A transposable element into the human long noncoding RNA CARMEN is a switch for cardiac precursor cell specification.

AIMS The major cardiac cell types composing the adult heart arise from common multipotent precursor cells. Cardiac lineage decisions are guided by extrinsic and cell-autonomous factors, including recently discovered long noncoding RNAs (lncRNAs). The human lncRNA CARMEN, which is known to dictate specification towards the cardiomyocyte (CM) and the smooth muscle cell (SMC) fates, generates a diversity of alternatively spliced isoforms. METHODS AND RESULTS The CARMEN locus can be manipulated to direct human primary cardiac precursor cells (CPCs) into specific cardiovascular fates. Investigating CARMEN isoform usage in differentiating CPCs represents therefore a unique opportunity to uncover isoform-specific function in lncRNAs. Here, we identify one CARMEN isoform, CARMEN-201, to be crucial for SMC commitment. CARMEN-201 activity is encoded within an alternatively-spliced exon containing a MIRc short interspersed nuclear element. This element binds the transcriptional repressor REST (RE1 Silencing Transcription Factor), targets it to cardiogenic loci, including ISL1, IRX1, IRX5, and SFRP1, and thereby blocks the CM gene program. In turn, genes regulating SMC differentiation are induced. CONCLUSIONS These data show how a critical physiological switch is wired by alternative splicing and functional transposable elements in a long noncoding RNA. They further demonstrated the crucial importance of the lncRNA isoform CARMEN-201 in SMC specification during heart development

Bladder cancer organoids as a functional system to model different disease stages and therapy response.

Bladder Cancer (BLCa) inter-patient heterogeneity is the primary cause of treatment failure, suggesting that patients could benefit from a more personalized treatment approach. Patient-derived organoids (PDOs) have been successfully used as a functional model for predicting drug response in different cancers. In our study, we establish PDO cultures from different BLCa stages and grades. PDOs preserve the histological and molecular heterogeneity of the parental tumors, including their multiclonal genetic landscapes, and consistently share key genetic alterations, mirroring tumor evolution in longitudinal sampling. Our drug screening pipeline is implemented using PDOs, testing standard-of-care and FDA-approved compounds for other tumors. Integrative analysis of drug response profiles with matched PDO genomic analysis is used to determine enrichment thresholds for candidate markers of therapy response and resistance. Finally, by assessing the clinical history of longitudinally sampled cases, we can determine whether the disease clonal evolution matched with drug response

Computational modeling of epigenetic regulation of gene expression during chronic inflammation

This study's aim is to analyze in detail a series of Next Generation Sequencing (NGS) experiments which were designed in order to characterize the transcriptome and the epigenetic regulation of gene expression during chronic inflammation. The experimental mouse model of Rheumatoid Arthritis (RA), TghuTNF was used as a model of chronic inflammation. The experiments were performed in 3 age groups (3, 8 and 11 weeks of age) corresponding to the distinct disease stages. Transcriptome was probed with the use of RNA-Seq and the epigenetic modifications were measured with ChIP-Seq and the use of the appropriate antibodies.Aiming to better understand and interpret the RNA-Seq results, a new computational method was designed and implemented. This method prioritizes regulatory factors and infers regulatory networks basen on a gene expression profile. Additionally, a second RNA-Seq experiment, which was designed to measure the expression of lowly abundant long non coding RNAs (lncRNAs), was analyzed. The protein coding genes were clustered based on their differential expression profile and functional enrichment analysis was performed in the respective clusters. The epigenetic modifications were quantified and their changes were associated with changes in gene expression.As a result, a genome-wide map of gene expression and epigenetic regulation of gene expression during chronic inflammation was created. Finally, the results were compared with respective results from RA patients showcasing the similarities of this experimental mouse model to the human disease of RA at a genome-wide level.Σκοπός της μελέτης είναι αρχικά η λεπτομερής ανάλυση των πειραμάτων νέων τεχνολογιών που έχουν σχεδιαστεί τόσο στο επίπεδο της γονιδιακής έκφρασης, όσο και σε αυτό των επιγενετικών τροποποιήσεων, κατά τη διάρκεια ανάπτυξης της χρόνιας φλεγμονής στο ζωικό πρότυπο της Ρευματοειδούς Αρθρίτιδας (ΡΑ), TghuTNF. Τα πειράματα διεξήχθησαν σε 3 ηλικιακές κατηγορίες (3, 8 και 11 εβδομάδες) που αντιστοιχούν στα 3 αντίστοιχα στάδια ανάπτυξης της ασθένειας. Η μέτρηση της γονιδιακής έκφρασης πραγματοποιήθηκε σε επίπεδο RNA (RNA-Seq) ενώ για τη μέτρηση των επιγενετικών τροποποιήσεων χρησιμοποιήθηκαν παραλλαγές της τεχνολογίας ChIP-Seq με τα αντίστοιχα αντισώματα. Για τη καλύτερη κατανόηση των αποτελεσμάτων του πειράματος RNA-Seq σχεδιάστηκε και υλοποιήθηκε μία υπολογιστική μέθοδος ιεράρχησης ρυθμιστικών παραγόντων και εξαγωγής ρυθμιστικών δικτύων. Επιπλέον, πραγματοποιήθηκε ανάλυση σε ένα δεύτερο πείραμα RNA-Seq ειδικά σχεδιασμένο για την ανίχνευση των χαμηλότερα εκφρασμένων μεγάλων μη κωδικών μεταγράφων (lncRNAs). Πραγματοποιήθηκε ομαδοποίηση των γονιδίων με βάση τη διαφορική τους έκφραση και λειτουργική ανάλυση για εμπλουτισμένες λειτουργικές κατηγορίες. Οι επιγενετικές τροποποιήσεις ποσοτικοποιήθηκαν και συσχετίστηκαν με τα επίπεδα γονιδιακής έκφρασης σύμφωνα με τις μέχρι τώρα ευρέως χρησιμοποιούμενες τεχνικές. Με αυτόν τον τρόπο δημιουργήθηκε ένας γενικός «χάρτης» γονιδιακής έκφρασης και επιγενετικών τροποποιήσεων κατά τη διάρκεια της ανάπτυξης χρόνιας φλεγμονής. Τέλος, πραγματοποιήθηκε σύγκριση των αποτελεσμάτων με αντίστοιχα από ασθενείς με ΡΑ αποδεικνύοντας την ομοιότητα του συγκεκριμένου μοντέλου με την ΡΑ και στο επίπεδο των αποτελεσμάτων πειραμάτων –ομικής

Υπολογιστική μοντελοποίηση της επιγενετικής ρύθμισης της γονιδιακής έκφρασης κατά την ανάπτυξη χρόνιας φλεγμονής

Σκοπός της μελέτης είναι αρχικά η λεπτομερής ανάλυση των πειραμάτων νέων τεχνολογιών που έχουν σχεδιαστεί τόσο στο επίπεδο της γονιδιακής έκφρασης, όσο και σε αυτό των επιγενετικών τροποποιήσεων, κατά τη διάρκεια ανάπτυξης της χρόνιας φλεγμονής στο ζωικό πρότυπο της Ρευματοειδούς Αρθρίτιδας (ΡΑ), TghuTNF. Τα πειράματα διεξήχθησαν σε 3 ηλικιακές κατηγορίες (3, 8 και 11 εβδομάδες) που αντιστοιχούν στα 3 αντίστοιχα στάδια ανάπτυξης της ασθένειας. Η μέτρηση της γονιδιακής έκφρασης πραγματοποιήθηκε σε επίπεδο RNA (RNA-Seq) ενώ για τη μέτρηση των επιγενετικών τροποποιήσεων χρησιμοποιήθηκαν παραλλαγές της τεχνολογίας ChIP-Seq με τα αντίστοιχα αντισώματα. Για τη καλύτερη κατανόηση των αποτελεσμάτων του πειράματος RNA-Seq σχεδιάστηκε και υλοποιήθηκε μία υπολογιστική μέθοδος ιεράρχησης ρυθμιστικών παραγόντων και εξαγωγής ρυθμιστικών δικτύων. Επιπλέον, πραγματοποιήθηκε ανάλυση σε ένα δεύτερο πείραμα RNA-Seq ειδικά σχεδιασμένο για την ανίχνευση των χαμηλότερα εκφρασμένων μεγάλων μη κωδικών μεταγράφων (lncRNAs). Πραγματοποιήθηκε ομαδοποίηση των γονιδίων με βάση τη διαφορική τους έκφραση και λειτουργική ανάλυση για εμπλουτισμένες λειτουργικές κατηγορίες. Οι επιγενετικές τροποποιήσεις ποσοτικοποιήθηκαν και συσχετίστηκαν με τα επίπεδα γονιδιακής έκφρασης σύμφωνα με τις μέχρι τώρα ευρέως χρησιμοποιούμενες τεχνικές. Με αυτόν τον τρόπο δημιουργήθηκε ένας γενικός «χάρτης» γονιδιακής έκφρασης και επιγενετικών τροποποιήσεων κατά τη διάρκεια της ανάπτυξης χρόνιας φλεγμονής. Τέλος, πραγματοποιήθηκε σύγκριση των αποτελεσμάτων με αντίστοιχα από ασθενείς με ΡΑ αποδεικνύοντας την ομοιότητα του συγκεκριμένου μοντέλου με την ΡΑ και στο επίπεδο των αποτελεσμάτων πειραμάτων –ομικής.This study's aim is to analyze in detail a series of Next Generation Sequencing (NGS) experiments which were designed in order to characterize the transcriptome and the epigenetic regulation of gene expression during chronic inflammation. The experimental mouse model of Rheumatoid Arthritis (RA), TghuTNF was used as a model of chronic inflammation. The experiments were performed in 3 age groups (3, 8 and 11 weeks of age) corresponding to the distinct disease stages. Transcriptome was probed with the use of RNA-Seq and the epigenetic modifications were measured with ChIP-Seq and the use of the appropriate antibodies.Aiming to better understand and interpret the RNA-Seq results, a new computational method was designed and implemented. This method prioritizes regulatory factors and infers regulatory networks basen on a gene expression profile. Additionally, a second RNA-Seq experiment, which was designed to measure the expression of lowly abundant long non coding RNAs (lncRNAs), was analyzed. The protein coding genes were clustered based on their differential expression profile and functional enrichment analysis was performed in the respective clusters. The epigenetic modifications were quantified and their changes were associated with changes in gene expression.As a result, a genome-wide map of gene expression and epigenetic regulation of gene expression during chronic inflammation was created. Finally, the results were compared with respective results from RA patients showcasing the similarities of this experimental mouse model to the human disease of RA at a genome-wide level

Inferring active regulatory networks from gene expression data using a combination of prior knowledge and enrichment analysis

Abstract Background Under both physiological and pathological conditions gene expression programs are shaped through the interplay of regulatory proteins and their gene targets, interactions between which form intricate gene regulatory networks (GRN). While the assessment of genome-wide expression for the complete set of genes at a given condition has become rather straight-forward and is performed routinely, we are still far from being able to infer the topology of gene regulation simply by analyzing its “descendant” expression profile. In this work we are trying to overcome the existing limitations for the inference and study of such regulatory networks. We are combining our approach with state-of-the-art gene set enrichment analyses in order to create a tool, called Regulatory Network Enrichment Analysis (RNEA) that will prioritize regulatory and functional characteristics of a genome-wide expression experiment. Results RNEA combines prior knowledge, originating from manual literature curation and small-scale experimental data, to construct a reference network of interactions and then uses enrichment analysis coupled with a two-level hierarchical parsing of the network, to infer the most relevant subnetwork for a given experimental setting. It is implemented as an R package, currently supporting human and mouse datasets and was herein tested on one test case for each of the two organisms. In both cases, RNEA’s gene set enrichment analysis was comparable to state-of-the-art methodologies. Moreover, through its distinguishing feature of regulatory subnetwork reconstruction, RNEA was able to define the key transcriptional regulators for the studied systems as supported from the literature. Conclusions RNEA constitutes a novel computational approach to obtain regulatory interactions directly from a genome-wide expression profile. Its simple implementation, with minimal requirements from the user is coupled with easy-to-parse enrichment lists and a subnetwork file that may be readily visualized to reveal the most important components of the regulatory hierarchy. The combination of prior information and novel concept of a hierarchical reconstruction of regulatory interactions makes RNEA a very useful tool for a first-level interpretation of gene expression profiles

Additional file 2: of Inferring active regulatory networks from gene expression data using a combination of prior knowledge and enrichment analysis

Mouse Test case results. Additional file 2 is a folder containing the detailed results of the Mouse Test case in HTML format. Each file includes the respective calculated enrichments for TFs, miRNAs, KEGG pathways, KEGG pathway categories and GO terms. In order to view the results a standard web-browser is needed (Chrome and Mozilla Firefox have been tested). The HTML files must be opened from inside the folder because additional files (images and javascripts) which are needed for the correct view of the results are included. (ZIP 83 kb